import numpy as np
import pandas as pd
import folium as fm
import json

volcano_map=fm.Map(max_bounds=True,min_zoom=2)
us_cities_map=fm.Map(location=[39.5,-98.35],max_bounds=True,min_zoom=4,zoom_start=4)
us_states_map=fm.Map(location=[39.5,-98.35],max_bounds=True,min_zoom=4,zoom_start=4)

volcanos=pd.read_csv('./volcano.csv')
volcanos.head()

	Year	Month	Day	TSU	EQ	Name	Location	Country	Latitude	Longitude	...	TOTAL_DEATHS	TOTAL_DEATHS_DESCRIPTION	TOTAL_MISSING	TOTAL_MISSING_DESCRIPTION	TOTAL_INJURIES	TOTAL_INJURIES_DESCRIPTION	TOTAL_DAMAGE_MILLIONS_DOLLARS	TOTAL_DAMAGE_DESCRIPTION	TOTAL_HOUSES_DESTROYED	TOTAL_HOUSES_DESTROYED_DESCRIPTION
0	2010	1	NaN	NaN	NaN	Tungurahua	Ecuador	Ecuador	-1.467	-78.442	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.0	NaN	NaN
1	2010	3	31.0	NaN	NaN	Eyjafjallajokull	Iceland-S	Iceland	63.630	-19.620	...	2.0	1.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2010	5	27.0	NaN	NaN	Pacaya	Guatemala	Guatemala	14.381	-90.601	...	1.0	1.0	3.0	1.0	NaN	NaN	NaN	1.0	3.0	1.0
3	2010	5	29.0	TSU	EQ	Sarigan	Mariana Is-C Pacific	United States	16.708	145.780	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	2010	8	6.0	NaN	NaN	Karangetang [Api Siau]	Sangihe Is-Indonesia	Indonesia	2.780	125.480	...	4.0	1.0	NaN	NaN	5.0	1.0	NaN	NaN	NaN	1.0

5 rows × 36 columns

volcanos_markers=fm.FeatureGroup()
for index, row in volcanos.iterrows():
    volcanos_markers.add_child(
        fm.Marker(
            location=[row['Latitude'],row['Longitude']],
            tooltip=fm.Tooltip(f"{row['Name']}, {row['Country']}"),
            icon=fm.Icon(color='red',icon='warning-sign')
        )
    )
    
volcano_map.add_child(volcanos_markers)

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us_cities_population=pd.read_csv('./US_Population_cities_2019.csv')

max_pop=us_cities_population['pop'].max()
min_pop=us_cities_population['pop'].min()

us_cities_population['relative_pop']=(us_cities_population['pop']-min_pop)/max_pop

us_cities_population['rank_pop']=us_cities_population['pop'].rank(pct=True)

def color_set(value):
    if value<=.25:
        return 'red'

    elif value<=.75:
        return'blue'
    
    else :
        return 'green'

us_cities_population['color']=us_cities_population['rank_pop'].apply(color_set)

us_cities_population.head()

	name	pop	lat	lon	relative_pop	rank_pop	color
0	New York	8287238	40.730599	-73.986581	0.999144	1.000000	green
1	Los Angeles	3826423	34.053717	-118.242727	0.460869	0.999690	green
2	Chicago	2705627	41.875555	-87.624421	0.325625	0.999380	green
3	Houston	2129784	29.758938	-95.367697	0.256140	0.999071	green
4	Philadelphia	1539313	39.952335	-75.163789	0.184889	0.998761	green

us_markers=fm.FeatureGroup()

for (index,city) in us_cities_population.iterrows():
    us_markers.add_child(
        fm.CircleMarker(
            location=[city['lat'],city['lon']],
            tooltip=fm.Tooltip(f"{city['name']}, {city['pop']}"),
            radius=city['relative_pop']*100,
            fill=True,
            opacity=0.8,
            color=city['color']
        )
    )

us_cities_map.add_child(us_markers)

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us_states_population=pd.read_csv('./US_Population_states_2019.csv',index_col='state')

us_states_population['rank_pop']=us_states_population['population'].rank(pct=True)

us_states_population['color']=us_states_population['rank_pop'].apply(color_set)

us_states_population.head()

	population	lat	long	rank_pop	color
state
Alabama	4903185	32.377716	-86.300568	0.549020	blue
Alaska	731545	58.301598	-134.420212	0.078431	red
Arizona	7278717	33.448143	-112.096962	0.745098	blue
Arkansas	3017804	34.746613	-92.288986	0.372549	blue
California	39512223	38.576668	-121.493629	1.000000	green

with open('./us_map.json','r',encoding='utf-8-sig') as file :
    geojson_states=json.load(file)


for state in geojson_states['features']:
    name=state['properties']['name']
    state['properties']['population']=f"{us_states_population.loc[name]['population']:,}"

us_states_map.add_child(
    fm.GeoJson(
        data=geojson_states,
        style_function = lambda feature: {'color': us_states_population.loc[feature['properties']['name']]['color']},
        tooltip=fm.GeoJsonTooltip(
                fields=['name','population'],
                aliases=['State: ','Population: ']
                ),
        )
    )

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